Knowledge library

This record captures one LangGraph forecasting run on whether the Federal Reserve would raise its target rate during 2026. Three agents produced probabilities, followed by a debate and a supervisor update. The logged outputs include an aggregate probability,…

This notebook uses double machine learning to test whether the association between the implied-minus-realized volatility spread and future equity returns persists after adjustment for observed confounders. It compares the adjusted estimate with naive…

This notebook assembles a research agent that searches for evidence and returns a structured probability for a binary question. It describes action parsing and validation, a turn budget, and a saved forecast artifact that records the rationale and supporting…

This notebook explains PCA as a feature-free latent-factor baseline for forecasting ETF returns. It decomposes each label’s training-window return matrix, using a loading for each fund and estimated factor premia to assemble forecasts. Because the same fund…

This module describes two components in a multi-agent forecasting pipeline. A debate agent alternates between bullish and bearish arguments, requiring each side to address the other’s claims and provide a probability estimate with supporting evidence. It…

This notebook shows how to use PCMCI to search for delayed relationships among ETF and volatility returns without specifying a causal graph in advance. It contrasts panel-based conditional-independence testing with pairwise Granger tests, which account for a…

This notebook explains how PatchTST uses fixed-length patches of sequential market features to forecast NASDAQ-100 labels. Attention relates patches across a trailing hour while reducing the sequence length compared with attention over every minute. The…

This document explains a CME futures experiment using TabM, a parameter-efficient neural ensemble, on the same engineered feature rows and walk-forward folds used by linear and gradient-boosting models. TabM shares most weights across ensemble members to…

The document presents NLinear, a minimal sequence model for forecasting NASDAQ-100 stock returns from intraday features. It uses a fixed trailing window, subtracts the latest observation so the model works with changes rather than levels, and maps the…

The document describes a pipeline for turning SEC 8-K filings into structured corporate events. A language model extracts event relationships, while explicit schema checks validate entity names, relation types, categories, and dates. Deterministic…

This module describes migrating a registry after a library changed the numbering of walk-forward folds while leaving their date windows and fitted values unchanged. Since fold identifiers appear throughout training specifications, prediction keys, input…

This notebook explains how hidden Markov models infer unobserved market states from returns and recent volatility. It first establishes simple comparison rules: a volatility index threshold for stress and price relative to a long moving average for trend. A…

This notebook presents a framework for checking whether a momentum signal’s apparent quality survives reasonable research choices. Using real ETF data before the designated holdout period, it varies one lookback parameter at a time and measures…

The notebook explains how to compare prediction models for S&P 500 options without allowing incomplete or mismatched evaluation samples to distort the results. It identifies each model by family, configuration, and checkpoint, checks that predictions meet…

This notebook builds uncertainty intervals for ETF forward returns using Ridge regression and three conformal methods: split conformal prediction, conformalized quantile regression, and adaptive conformal inference. It evaluates them with walk-forward folds…

This notebook uses double machine learning to estimate whether a perpetual-futures premium z-score is associated with the following eight-hour return after adjustment for six pre-treatment controls. It compares effects across high- and low-volatility regimes…

This notebook compares linear models on ETF features built from overlapping return windows and related momentum, volatility, and oscillator measures. Such predictors are collinear, making ordinary least squares coefficients unstable. The experiment fits…

Fractional differencing applies a non-integer power of the lag difference operator to log prices. Its binomial weights decay across past observations, so the result combines price changes with some surviving level information. The fractional order controls…

This chapter maps financial data across market, fundamental, and alternative sources, then compares how asset classes shape what prices, liquidity, and observations mean. It emphasizes that timestamps, adjustments, identifiers, and revisions are part of a…

This notebook presents TimeGAN, a recurrent generative adversarial model for producing synthetic financial sequences. Its five modules learn an embedding and reconstruction, predict successive latent states, generate sequences from noise, and distinguish…

This document explains a shared method for generating walk-forward validation splits from case study evaluation settings. It delegates fold construction to a calendar-aware splitter, works from unique timestamps so panel rows stay aligned, and steps backward…

This notebook applies two sequential detectors to real validation prediction errors from two US equities linear-model configurations over a calendar year. A two-window mean-shift detector looks for changes in error magnitude, while a bad-day frequency…

This notebook presents a supervised autoencoder for predicting ETF forward returns. The network compresses a fund’s feature row into a low-dimensional representation and predicts returns directly from that representation. Unlike conditional autoencoders in…

This document describes how to derive a single holdout training interval from an existing walk-forward validation specification. It takes the declared holdout dates from the study configuration, uses the earliest training start across validation folds, and…